Deep belief networks (DBNs) stand as significant milestones in the history of deep learning, marking crucial advancements in our understanding and application of artificial intelligence. In this course, Diving Deep into Deep Belief Networks (DBNs), you’ll gain an understand of DBN architecture and see use cases to solve real-world data analysis challenges. First, you’ll explore the architecture and functioning of restricted Boltzmann machines (RBMs), the building blocks of DBNs, understanding their unique role in unsupervised learning and feature extraction. Next, you’ll discover how to stack RBMs to form deep belief networks, and how to use concepts like “contrastive divergence” and “Gibbs sampling.” Finally, you’ll learn how to optimize your networks, either using regularization tools or fine-tuning the model. When you’re finished with this course, you’ll have the skills and knowledge of deep belief networks needed to effectively use them in projects and unlock new possibilities in data analysis.
Author Name: Alper Tellioglu
Author Description:
I am a developer, and passionate about technology in all kind. That’s why I worked on several development areas in the past, and still exploring other technologies and frameworks that catch my interest. Besides engineering, I am interested in design and art. I like building things with small and dynamic teams. Working in startup environment keeps me motivated!